Early Crop Mapping Using Dynamic Ecoregion Clustering: Comparison
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Mapping target crops earlier than the harvest period is an essential task for improving agricultural productivity and decision-making. Early crop mapping provides valuable information for crop management, such as predicting yield, monitoring crop growth, and identifying areas with high production potential.

  • early crop mapping
  • ecoregions
  • cropland data layer
  • MODIS
  • NDVI
  • EVI

1. Introduction

Mapping target crops earlier than the harvest period is an essential task for improving agricultural productivity and decision-making. Early crop mapping provides valuable information for crop management, such as predicting yield [1], monitoring crop growth [2[2][3],3], and identifying areas with high production potential [4]. In recent years, the application of remote sensing techniques to early crop mapping has gained widespread popularity, owing to its inherent advantages of non-invasiveness and rapid data acquisition. By utilizing multispectral imagery (MSI) captured by satellites like Landsat-8 [5,6[5][6][7],7], Sentinel-2 [7[7][8],8], and MODIS [6,9[6][9][10],10], valuable insights into crop health, vegetation indices, and land cover classification can be obtained. These datasets play a pivotal role in assessing crop vigor, identifying stress factors, and mapping crop types, and ultimately facilitating more informed agricultural decision-making.
Among the remote sensing techniques used in early crop mapping, the NDVI and EVI have emerged as widely employed indicators for monitoring vegetation growth and identifying different crop types. NDVI quantifies the greenness of vegetation based on the difference between near-infrared and red spectral bands, allowing for the detection of vegetation density and health. Similarly, EVI incorporates additional spectral bands to mitigate the influence of atmospheric and canopy background effects, providing enhanced accuracy in characterizing vegetation dynamics. Numerous studies have been conducted to develop and improve early crop mapping methods using NDVI and EVI data. For instance, both the harmonized time-series NDVI and EVI from Landsat-8 and Sentinel-2 data are used with the decision tree method for crop identification [11]. A time series analysis of MODIS NDVI data is utilized to map crop types for the US central great plains [9]. They applied an unsupervised classification method (ISODATA) to the 15-date NDVI time series to produce the crop or non-crop map. Similarly, ref. [12] used a multi-temporal MODIS NDVI approach to map soybeans in the USA for 2015. Furthermore, ref. [13] proves that the general crop maps produced using the MODIS EVI and NDVI data both had very high overall (97.0%) and class-specific user’s and producer’s accuracies (ranging from 95 to 100%) for a case study for southwest Kansas.
However, accurately mapping crops at a large scale, such as for the entire CONUS, is challenging due to the heterogeneous nature of the crop-growing environment. This challenge arises from the intricate interactions among soil type, climate, and topography, which significantly influence the success of crop mapping [10,14,15,16,17,18][10][14][15][16][17][18]. The crop-growing environment displays substantial spatial and temporal heterogeneity. Soil types vary across regions, impacting vegetation growth and the reflectance captured by remote sensing data [10,15,18][10][15][18]. Additionally, climate conditions, including temperature, precipitation, evaporation, wind speed, rainfall, and solar radiation, exhibit considerable variations, affecting crop phenology and productivity [10,14,15,16,17,18][10][14][15][16][17][18]. Moreover, topographic features, such as slope, aspect, and elevation, play a vital role in determining crop growth patterns by influencing factors like solar radiation distribution, water availability, and wind patterns [10,15,18][10][15][18]. The failure to account for these influential factors in the crop mapping process can lead to inaccurate results, particularly when applied to large-scale areas. To address this issue, the concept of ecoregions was introduced. Ecoregions, which are geographically distinct areas with unique ecological patterns and processes, provide a valuable framework for large-scale crop mapping. By integrating knowledge of ecological characteristics within each ecoregion, such as climate, geology, and topography, it becomes possible to identify suitable crop-growing areas and predict crop distribution based on shared environmental characteristics. By leveraging the ecoregional context and incorporating factors like climate, soil types, and topography, crop mapping efforts can be tailored to effectively account for the heterogeneity of the crop-growing environment, leading to improved mapping accuracy at a large scale. Therefore, ecoregion clustering methods have been developed to address this issue [19], and the static ecoregion clustering method [20] was used for crop mapping by [10]. The ecoregion clustering method involves analyzing environmental factors, such as soil type, climate, and topography, to divide the study area into multiple ecoregions. Each ecoregion has its own unique characteristics that affect crop growth, and these characteristics are taken into account during classification. This approach has been shown to improve crop mapping accuracy compared to country and state-level mapping [10].
Nevertheless, aside from the large-scale problem, the inter-year challenge is also of great significance for precise crop mapping. The climate variation between different years within a given region is anticipated to significantly influence the patterns of Vegetation Indices (VIs) related to crop growth and, consequently, crop-mapping outcomes. The static ecoregion clustering method only provides a single ecoregion map for the CONUS. They proceed based on the assumption that the ecoregion remains unchanged over different years.

2. Early Crop Mapping Using Dynamic Ecoregion Clustering

Table 1 presents various methods related to crop mapping, along with the factors they consider, the datasets utilized, the vegetation indices employed, and the specific research regions. Various classification algorithms, including unsupervised methods like k-means clustering and supervised methods like decision trees and deep learning approaches, have been utilized for crop classification at small scales using Landsat-8 and Sentinel-2A data.
Table 1.
The methods of related work.
Factors Taken into Consideration Method Dataset Vegetation Indices Research Region
Soil, Climate, and Topography Conditions [15] Sentinel-2 NDVI, EVI, LSWI, RGVI2 Shanwei, Guangzhou (China)
- [11] Landsat-8 and Sentinel-2 NDVI, EVI Four subregions in Kansas (USA), Gaoteng (South Africa), Punjab (India), Shandong (China)
- [21] Sentinel-2 Spectral bands, NDVI Gharb plain (northwest of Morocco)
- [22] MODIS Phenological and other variables 22 States in the USA
- [23] MODIS NDVI, GCVI, EVI, LSWI Champaign County, Illinois (USA)
GDD, Precipitation [16] Landsat-7 Spectral bands Doniphan County, Kansas (USA)
GDD [17] MODIS NDVI Kansas (USA)
Precipitation, Soil conditions (within administrative demarcations) [18] MODIS NDVI The entire CONUS
Soil, Climate, and Topography Conditions [10] MODIS NDVI The entire CONUS
One study [15] focuses on utilizing the Random Forest algorithm for mapping and predicting rice yield through analysis of Sentinel-2 satellite data. Another research [11] effort concentrates on creating a high-resolution crop intensity mapping methodology by integrating data from Landsat-8 and Sentinel-2 satellites using a random forest algorithm. Furthermore, a hybrid deep-learning architecture called CerealNet [21] has been introduced for the specific purpose of cereal crop mapping, utilizing Sentinel-2 time-series data. However, ref. [21] has limitations in its scope, as it specifically examines a research region characterized by a hot Mediterranean climate with dry summers. This region selection addresses a common challenge posed by cloud cover in time-series data analysis of Landsat-8 and Sentinel-2 images. Cloud cover plays a crucial role in crop mapping by impacting the availability and quality of remote sensing data. The presence of clouds obstructs satellite imagery, leading to the loss or concealment of vital information about the Earth’s surface. Consequently, these cloud-induced data gaps undermine the accuracy and reliability of crop-mapping results. The negative impact of cloud cover on crop mapping becomes even more pronounced when considering large-scale applications, such as mapping crops across the entire CONUS. The expansive coverage of such regions makes them more susceptible to varying cloud cover patterns, resulting in extensive areas with missing or incomplete data. This increases the risk of biased or inaccurate crop classification, making it challenging to obtain a comprehensive and reliable understanding of crop distribution and dynamics at a broader scale. In order to overcome this limitation of Landsat-8 and Sentinel-2 images, some works focus on using time-series MODIS data for early crop mapping. MODIS data can overcome the limitations imposed by cloud cover on crop mapping due to its unique capabilities. Its moderate spatial resolution allows for wider coverage and reduces the impact of cloud cover, while its frequent revisit time ensures more opportunities to capture cloud-free images, enabling a more consistent and reliable monitoring of crop patterns at regional or global scales. Previous research [22,23][22][23] has shown that individual major crops, such as corn and soybeans, can be mapped accurately as early as July and August using MODIS dataset. However, these studies were also limited in scope, focusing on counties, states, or groups of states. A crop mapping model that is limited in scope cannot be directly applied to a large-scale setting due to spatial differences. The presence of spatial heterogeneity, encompassing diverse factors like soil types, climate, topography, and other environmental elements, significantly impacts crop growth patterns and diminishes the model’s accuracy outside of its intended region. Overcoming this challenge necessitates the integration of spatially explicit information into the method, effectively capturing and using the spatial variations and encompassing the diverse conditions present across the target area. There are some works [16,17][16][17], that use Growing-degree-day (GDD), which is a valuable metric that quantifies the accumulated heat necessary for the growth and development of vegetation. Its significance lies in its crop-specific nature, as the magnitude of GDD required during different growing stages varies for each crop. This characteristic makes GDD a valuable tool in crop classification, as it provides insights into the progression and timing of crop growth. By considering the crop-specific GDD thresholds for various stages, it becomes possible to leverage this metric for accurate and effective crop classification and monitoring. Other studies have performed crop classifications at smaller administrative units such as Agriculture Statistics Counties and Districts [24], states [22], or Agroecological zones [18]. Nonetheless, these methodologies either disregard fluctuations in precipitation and soil characteristics or are conducted within administrative or political demarcations that lack relevance to crop phenology. Alternatively, they encompass areas of such magnitude that they fail to encompass the nuanced phenological fluctuations driven by climatic variations. An ideal approach would be to model regions based on environmental variables that reflect crop growing conditions and are of small size, created using quantitative analytical methods that are both empirical and reproducible. Multivariate Geographic Clustering (MGC) algorithms [19] and multivariate spatio-temporal clustering (MSTC) [10,20][10][20] have been successfully used to create ecoregions that exist within similar combinations of ecologically relevant conditions such as temperature, precipitation, soil, and topographic properties on a map. However, an identified limitation of the study [10] is that they assume that the ecoregion boundaries remain constant, which may not accurately represent the true variability. In addition, they solely present a single 500-ecoregions map directly utilizing MSTC without incorporating any decision-making process to determine the optimal number of ecoregions.

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